class_docstr: Transformer with Masked Language Model
docstr: On Epoch End
docstr_tok: ["On","Epoch","End"]
code: def training_epoch_end(self, output):
      """On Epoch End"""
      if self.hparams.attention_type == "performer":
          self.transformer.redraw_projection_matrix(-1)
  
imports: from abc import ABC
  from base64 import decode
  from math import gamma
  import torch
  import torch.nn
  import torch.nn.functional
  import torchmetrics
  import pickle
  import pytorch_lightning
  from pytorch_lightning import seed_everything
  import os
  from pathlib import Path
  import logging
  from src.transformer.transformer_utils import *
  from src.transformer.transformer import Transformer, MaskedLanguageModel, CLS_Decoder
label_desc: Use image node_id 6 for calling the TransformerEncoder obj's underlying member method code with example usage: obj.training_epoch_end(output) without return types